{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import brier_score_loss\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.calibration import CalibratedClassifierCV,calibration_curve\n",
    "from sklearn import metrics\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=9000,n_features=20,class_sep=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LinearSVC()\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde072f6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wgDHLYzXGh/512jjxCEbvxzTz37rnZMP4II3EmGmqLuG15dq2pryKkcC/Mx+vkhYaM024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\nnHdRGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde072f63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "fig,axes = plt.subplots(1,3,sharey=True)\n",
    "\n",
    "y_preds = clf.decision_function(X_test)\n",
    "\n",
    "clf_preds = y_preds.reshape(-1,1)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, clf_preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "axes[0].set_title('ROC Curve - original classifier')\n",
    "axes[0].plot(fpr, tpr, label='AUC = {:.5f}'.format(auc_score))\n",
    "\n",
    "axes[0].plot([0,1],[0,1],'k:')\n",
    "\n",
    "axes[0].set_xlim([-0.1,1.1])\n",
    "axes[0].set_ylim([-0.1,1.1])\n",
    "axes[0].set_ylabel('True Positive Rate')\n",
    "axes[0].set_xlabel('False Positive Rate')\n",
    "\n",
    "axes[0].legend(loc='lower right')\n",
    "\n",
    "\n",
    "## CCV sigmoid\n",
    "\n",
    "ccv_sig = CalibratedClassifierCV(clf,method='sigmoid',cv='prefit')\n",
    "ccv_sig.fit(X_train,y_train)\n",
    "\n",
    "y_preds = ccv_sig.predict_proba(X_test)\n",
    "\n",
    "ccv_preds_sig = y_preds[:,1]\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, ccv_preds_sig)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "axes[1].set_title('ROC Curve - Sigmoid Calibration')\n",
    "axes[1].plot(fpr, tpr, label='AUC = {:.5f}'.format(auc_score))\n",
    "\n",
    "axes[1].plot([0,1],[0,1],'k:')\n",
    "\n",
    "axes[1].set_xlim([-0.1,1.1])\n",
    "axes[1].set_ylim([-0.1,1.1])\n",
    "axes[1].set_ylabel('True Positive Rate')\n",
    "axes[1].set_xlabel('False Positive Rate')\n",
    "\n",
    "axes[1].legend(loc='lower right')\n",
    "\n",
    "\n",
    "## CCV isotonic\n",
    "\n",
    "ccv_iso = CalibratedClassifierCV(clf,method='isotonic',cv='prefit')\n",
    "ccv_iso.fit(X_train,y_train)\n",
    "\n",
    "y_preds = ccv_iso.predict_proba(X_test)\n",
    "\n",
    "ccv_preds_iso = y_preds[:,1]\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, ccv_preds_iso)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "axes[2].set_title('ROC Curve - Isotonic Calibration')\n",
    "axes[2].plot(fpr, tpr, label='AUC = {:.5f}'.format(auc_score))\n",
    "\n",
    "axes[2].plot([0,1],[0,1],'k:')\n",
    "\n",
    "axes[2].set_xlim([-0.1,1.1])\n",
    "axes[2].set_ylim([-0.1,1.1])\n",
    "axes[2].set_ylabel('True Positive Rate')\n",
    "axes[2].set_xlabel('False Positive Rate')\n",
    "\n",
    "axes[2].legend(loc='lower right')\n",
    "\n",
    "\n",
    "plt.gcf().set_size_inches(15,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.33145523],\n",
       "       [-0.36691291],\n",
       "       [-0.17986786],\n",
       "       ...,\n",
       "       [ 0.22549232],\n",
       "       [ 0.33746451],\n",
       "       [ 0.68203208]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.596224474271525"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_preds.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.285882731004038"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_preds.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_preds_normalized = (clf_preds+(clf_preds.min()*-1))\n",
    "clf_preds_normalized = clf_preds_normalized/clf_preds_normalized.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_preds_normalized.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde07448390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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KiKaHqla1+PxyQZ8+VuEuSghX3OjUKXqY2qpVqjVqFL3I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\nSCQSiUQCyIaxRCKRSCQSiUQCwP8HbC+OJjRMUPcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde07256588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "fig, axes = plt.subplots(1,2)\n",
    "\n",
    "ax=axes[0]\n",
    "\n",
    "ax.set_xlim([-0.1,1.1])\n",
    "ax.set_ylim([-0.1,1.1])\n",
    "\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfect calibration\")\n",
    "\n",
    "clf_score = brier_score_loss(y_test, clf_preds_normalized, pos_label=1)\n",
    "fraction_of_positives, mean_predicted_value = calibration_curve(y_test, clf_preds_normalized, n_bins=30)\n",
    "ax.plot(mean_predicted_value, fraction_of_positives, \"r-\", label=\"No Calibration (Brier loss={:.3f})\".format(clf_score))\n",
    "\n",
    "clf_score = brier_score_loss(y_test, ccv_preds_sig, pos_label=1)\n",
    "fraction_of_positives, mean_predicted_value = calibration_curve(y_test, ccv_preds_sig, n_bins=30)\n",
    "ax.plot(mean_predicted_value, fraction_of_positives, \"b-\", label=\"Sigmoid Calibration (Brier loss={:.3f})\".format(clf_score))\n",
    "\n",
    "ax.legend(loc='lower right')\n",
    "ax.set_title('Original vs Sigmoid Calibration - \\n Linear SVM Classifier', size=16)\n",
    "plt.subplots_adjust(top=0.85)\n",
    "\n",
    "\n",
    "## ax 2\n",
    "\n",
    "ax=axes[1]\n",
    "\n",
    "ax.set_xlim([-0.1,1.1])\n",
    "ax.set_ylim([-0.1,1.1])\n",
    "\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfect calibration\")\n",
    "\n",
    "clf_score = brier_score_loss(y_test, clf_preds_normalized, pos_label=1)\n",
    "fraction_of_positives, mean_predicted_value = calibration_curve(y_test, clf_preds_normalized, n_bins=30)\n",
    "ax.plot(mean_predicted_value, fraction_of_positives, \"r-\", label=\"No Calibration (Brier loss={:.3f})\".format(clf_score))\n",
    "\n",
    "clf_score = brier_score_loss(y_test, ccv_preds_iso, pos_label=1)\n",
    "fraction_of_positives, mean_predicted_value = calibration_curve(y_test, ccv_preds_iso, n_bins=30)\n",
    "ax.plot(mean_predicted_value, fraction_of_positives, \"b-\", label=\"Isotonic Calibration (Brier loss={:.3f})\".format(clf_score))\n",
    "\n",
    "ax.legend(loc='lower right')\n",
    "ax.set_title('Original vs Isotonic Calibration - \\n Linear SVM Classifier', size=16)\n",
    "plt.subplots_adjust(top=0.85)\n",
    "\n",
    "plt.gcf().set_size_inches(12,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Global TF Kernel (Python 3)",
   "language": "python",
   "name": "global-tf-python-3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
